Wi-Fi based indoor localization systems have fostered a growing interest and gained ample attention from researchers lately because of the ubiquitous and inexpensive nature of the required infrastructure. Different technologies are implied with a varying mix of accuracy, stability and challenges such as signal propagation models with trilateration and location fingerprint. Within a room, the signal propagation model works fairly. However due to complicated environment indoor settings and the random effects of signal propagation, it is extremely difficult to build an effective general model of signal propagation that coincides with the real world situation. For Wi-Fi fingerprinting, fine-grained supervised training is normally required to achieve high accuracy and resolution. The database generation, supervised training and deployment requirements are some disadvantages of location fingerprint.
Further, RF based indoor localization solutions require extensive training for fingerprinting, as all of them are supervised machine learning based approaches.